Grab

Information Technology And Services

SeniorDataScientist(LLMPost-Training)

Bangalore, India FULL TIME
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for mid candidates.

The Brief

“Senior Data Scientist (LLM Post-Training) at Grab. Skills: LLM Post-Training, Machine Learning, Deep Learning, Python. Implement post-training and optimisation tasks for LLMs. Handle domain-specific data processes”

What You'll Achieve.

Solutions that improve risk and safety measures; Enhance customer support; Personalise user experiences; Boost internal productivity; Solutions with measurable commercial impact

Industry & Context.

Information Technology And Services

What They're Looking For.

Must Have

Master's degree in computer science, artificial intelligence, or a related field, Solid theoretical foundation in machine learning and deep learning, In-depth understanding of large models, Proficient in advanced machine learning and deep learning principles, Proficient in large-language-model post-training techniques such as SFT (Supervised Fine-Tuning) and DPO (Direct Preference Optimisation), Experience managing large-scale training data, Experience in end-to-end deployment of applications guided by large models, Proficiency in Python programming, Competency in widely-used deep learning frameworks, Proficient in spoken and written English

Nice to Have

Experience in participation and contribution to open-source communities, Publication experience at top conferences, Experience in end-to-end LLM application deployment, Familiarised with all integration and optimisation challenges, Good technical thought process, Understanding of business

What You'll Do.

Implement post-training and optimisation tasks for LLMs

Handle domain-specific data processes

Develop systems integrating reasoning

multi-modal processing

multi-lingual modelling

and AI agent behaviour

Deploy LLM-based applications

Prepare technical documentation

Engage with business and product teams

How You'll Work.

Team & Collaboration

Collaborate with global teams; Engage with business and product teams

Communication Scope

Spoken and written English

Full Job Description

About Grab and our workplace Grab is Southeast Asia's leading superapp. We are dedicated to improving the lives of millions of users across the region by providing them everyday services such as deliveries, mobility, financial services, enterprise services and others. More than that, we provide the opportunity for them to have a better life. And that aspiration starts inside Grab because we believe in a seamless blend of work and home life, making every aspect of life better for all. Guided by The Grab Way, which spells out our mission, how we believe we can achieve it, and our operating principles—the 4Hs: Heart, Hunger, Honour and Humility—we work to create economic empowerment for the people of Southeast Asia. With our unwavering commitment to our values, we believe that we're more than a service provider; we're agents of positive change. Get to know the role We're looking for Data Scientists specialising in post-training of large language models (LLMs) to join our team, working onsite in Bangalore. You'll be part of an enthusiastic and collaborative team, working as an individual contributor, reporting to the Head of Data Science, Integrity & COREX who is based at our One North Singapore office. Your work will involve advancing fields such as reasoning, multi-modality, multi-lingual systems, and AI agents. You'll contribute to solutions that improve risk and safety measures, enhance customer support, personalise user experiences, and boost internal productivity. You'll work on end-to-end system deployments while collaborating with businesses and product teams to create solutions with measurable commercial impact. Responsibilities: * Implement post-training and optimisation tasks for large language models, including fine-tuning, distillation, performance evaluation, and low-resource training. * Handle domain-specific data processes such as data collection, cleaning, labelling, augmentation, and synthetic data generation for model improvement. * Develop systems th

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